Difference between revisions of "French gender cues"

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==Independent variables==
 
==Independent variables==
#We are using a pretest-posttest design to measure the overall effects of the online training.  We compare gain scores from students in the traditional course with no gender training with gain scores for students in the online course with gender training.
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#We are also tracking the relative ease of learning particular cues in terms of how [[reliability]] interacts with lexical and cue frequency.
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First, to ensure that the training is working, we are using a pretest-posttest design to measure the overall effects of the online training.  We compare scores from students in the traditional course with no gender training with scores for students in the online course with gender training. We may use d' measures instead of point or percentage differentials to account for a possible masculine default and general problems with the binary choice task.
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In order to predict how a given participant will perform in using a particular rule, we use two basic categories of independent variables: word-level and also participant-level. A sample of more specific (tentative) independent variables:
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#An individual's pre-test score, past performance history, and time on task;
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#The relative ease of learning particular cues in terms of how [[reliability]] interacts with lexical and cue frequency (In this study, because all stimuli presented follow the given cues, special attention should be paid to how cue conflicts within a given word influence gender choice);
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#A word's cognate status, or whether the cue is semantic in nature (such that it would carry independent information).
  
 
==Hypotheses==
 
==Hypotheses==

Revision as of 05:30, 27 March 2007

PIs Presson, MacWhinney
Faculty MacWhinney
Postdocs Pavlik
Others with > 160 hours n/a
Learnlab French
Number of participants ~40
Total Participant Hours ~40
Datashop? Expected 5/15/07

Abstract

The goal of this project is to improve the ability of students of Elementary French to determine the gender of French nouns. Like other studies conducted by MacWhinney and Pavlik, this work emphasizes the role of scheduling in attaining mastery.

Glossary

Research question

This research is designed to discover the best method of producing robust learning of French nominal gender.

Background and significance

Tucker, Lambert and Rigault (1977) evaluated the L1 (first language) learning of cues to gender in French. More recently, Holmes and Dejean de la Batie (1999) produced the first study of the acquisition of grammatical gender by L2 learners. Holmes and Segui (2004) have extended the detail of these analyses, but so far only with native speakers. Carroll (1999) and Lyster (2006) have explored the role of cue validity and availability in predicting usage by learners. All of these studies underscore the importance of high validity cues for the general vocabulary. However, these cues are only marginally useful for the highest frequency forms, whose gender must be learned more or less by rote. These analyses are in very close accord with the claims of the Competition Model (MacWhinney 1978, 2006). Our goal here is to use these findings to guide effective instruction.


Dependent variables

One primary dependent variable is percentage correct gender judgment for a given rule. Because there are only two genders in French, chance performance is at 50%.

Other possible dependent variables are latencies, percentage correct across rules, and post-test score.

Independent variables

First, to ensure that the training is working, we are using a pretest-posttest design to measure the overall effects of the online training. We compare scores from students in the traditional course with no gender training with scores for students in the online course with gender training. We may use d' measures instead of point or percentage differentials to account for a possible masculine default and general problems with the binary choice task.

In order to predict how a given participant will perform in using a particular rule, we use two basic categories of independent variables: word-level and also participant-level. A sample of more specific (tentative) independent variables:

  1. An individual's pre-test score, past performance history, and time on task;
  2. The relative ease of learning particular cues in terms of how reliability interacts with lexical and cue frequency (In this study, because all stimuli presented follow the given cues, special attention should be paid to how cue conflicts within a given word influence gender choice);
  3. A word's cognate status, or whether the cue is semantic in nature (such that it would carry independent information).

Hypotheses

  1. Learning will be most robust if high reliability cues are taught before low reliability cues or rote training.
  2. Mastery training with scheduling is more effective than simple repetition.
  3. Cues that do not interact with similar cues will be easier to learn than those that interact with other cues.

These predictions derive from the Competition Model (MacWhinney, in press).

Explanation

The Competition Model explanation for these effects emphasizes the role of cue reliability, cue availability, and lexical learning as determinants of gender cue learning. Availability and reliability are measured across the vocabulary.

Descendents

Annotated bibliography

  • Carroll, S. (1999). Input and SLA: Adults' sensitivity to different sorts of cues to French gender. Language Learning, 49, 37-92.
  • Holmes, V. M., & Dejean de la Batie, B. (1999). Assignment of grammatical gender by native speakers and foreign learners of French. Applied Psycholinguistics, 20, 479-506.
  • Holmes, V. M., & Segui, J. (2004). Sublexical and lexical influences on gender assignment in French. Journal of Psycholinguistic Research, 33, 425-457.
  • Lyster, R. (2006). Predictability in French gender attribution: A corpus analysis. French Language Studies, 16, 69-92.
  • MacWhinney, B. (2006). A unified model. In N. Ellis & P. Robinson (Eds.), Handbook of Cognitive Linguistics and Second Language Acquisition. Mahwah, NJ: Lawrence Erlbaum Press.
  • Pavlik, P. (2005). Modeling order effects in the learning of information.